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Status: Unchecked ×Keyword: deep learning protein design ×Clear all

3 claims from 1 paper

1. Biochemistry, Genetics and Molecular Biology › Protein Structure and Dynamics

   Computational Redesign of an Antifreeze Protein Using Deep Learning

   Calia, Altunc, Eufemio et al. · bioRxiv (Cold Spring Harbor Laboratory) · 2026

   Researchers used the deep learning tool ProteinMPNN to redesign a fish antifreeze protein, and all five variants tested were made in E. coli and had some antifreeze activity, though none matched the original.

   Unchecked3 claims
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   1. UncheckedOne of five redesigned antifreeze protein variants was predicted, and then experimentally shown, to be more stable at higher temperatures.“One design was predicted and experimentally confirmed to have increased thermostability.”
   2. UncheckedAll five redesigned versions of the fish antifreeze protein AFPIII showed measurable thermal hysteresis, an ice-growth-blocking activity, in the authors' tests.“All five variants displayed measurable thermal hysteresis activity.”
   3. UncheckedNone of five redesigned AFPIII variants matched wild-type activity, suggesting the known ice-binding residues alone may not fully preserve function.“However, none reached the activity of wild-type AFPIII, suggesting that maintaining the currently established set of ice-binding residues is not sufficient to fully preserve this AFP’s function; other, unidentified residues can also impact its activity.”